Migration Planning with Cached Dependency Data

Migrations — framework upgrades, database schema changes, API version bumps — require comprehensive impact analysis across the codebase. Multiple planners need the same dependency data, the same API inventories, and the same usage patterns. With org-shared cache, you run the analysis once and every planner works from the same foundation.

Use this page when

• You are planning codebase migrations and want AI assistance backed by cached dependency analysis.
• You need to understand how cached dependency graphs and impact analysis speed up migration planning.
• You want to configure which migration-relevant data (import maps, version constraints, API surfaces) feeds the cache.

Primary audience

• Primary: Technical Engineers
• Secondary: AI Agents, Technical Leaders

The Migration Planning Problem

In a 100+ engineer organization, migrations touch many teams and services. Planning a major framework upgrade involves:

• Identifying every service using the current framework version
• Mapping API surface changes between versions
• Cataloging breaking changes and their impact locations
• Estimating effort per service based on usage patterns
• Coordinating across team boundaries

Each of these steps requires AI analysis of the codebase. Without shared cache, every planner who asks "which services use framework X?" triggers an independent codebase scan.

How Cached Data Accelerates Planning

Dependency Graph for Impact Analysis

The cached dependency graph shows every package, its version, and its consumers. When you ask "what's the impact of upgrading React from 18 to 19?", the AI:

1. Checks the cached dependency graph for all React 18 consumers
2. Identifies breaking API changes between versions
3. Cross-references breaking changes against cached usage patterns
4. Produces an impact report per service

Steps 1 and 4 use cached data. Only step 2 (external changelog analysis) requires fresh upstream calls.

API Inventory for Version Bumps

When planning an API version bump, you need to know:

• Which consumers call the endpoints being changed
• What request/response schemas are affected
• Which integration tests cover the changing surface
• What client libraries need updates

The cached API inventory answers all of these questions without regenerating the endpoint catalog.

Configuring Migration Planning Cache

cache:
  org_shared:
    categories:
      - dependency_graphs
      - api_inventories
      - usage_patterns
      - test_maps
      - symbol_indexes
    ttl: 24h
    scope: organization

A 24-hour TTL works well for migration planning because planners typically work across several days, and the codebase state they're planning against shouldn't drift significantly during a planning cycle.

Migration Scenarios

Framework Upgrade Planning

You're planning to upgrade your HTTP framework across 12 services. The planning process:

Lead planner asks: "Which services depend on Axum 0.7 and what features do they use?"

The AI scans all service manifests and code patterns, producing a detailed usage report. This gets cached.

Team leads (12 of them) each ask: "What does my service need to change for the Axum upgrade?"

Each team lead's query filters the cached overall analysis to their specific service. Twelve queries, one upstream analysis.

Platform team asks: "What shared libraries need upgrading first?"

The AI references the cached dependency graph to identify shared packages that depend on the framework — answering from cache since the dependency relationships are already mapped.

Database Migration Planning

Planning a major schema migration requires understanding:

• Which services read/write to the affected tables
• What queries reference the changing columns
• Which data access patterns break with the new schema
• What order services should migrate in

Database engineer asks: "Map all services that query the users table and which columns they access"

This generates a comprehensive data access map that gets cached. Every subsequent question about the migration's service impact resolves from this cached map.

Application engineers ask: "What changes does my service need for the new user schema?"

The cached data access map instantly shows each service's specific exposure to the schema change.

API Version Bump Planning

Moving from API v1 to v2 requires coordinating consumers:

API owner asks: "List all consumers of /v1/orders endpoints and their usage patterns"

The cached API inventory and symbol indexes provide consumer mapping. This gets cached for the entire planning cycle.

Consumer teams ask: "What changes does my service need for the v2 migration?"

Each consumer team's query resolves from the cached consumer map and API diff analysis.

Multi-Planner Cost Impact

For a framework migration planned by eight people over two weeks:

Metric	Without Cache	With Org Cache
Planning queries	80-120	80-120
Upstream LLM calls	80-120	15-25
Cache hit rate	0%	78-85%
Token spend	$30-50	$6-12
Planning duration	2 weeks	1 week

The timeline compression comes from planners getting instant impact analysis instead of waiting for fresh codebase scans. When twelve team leads can independently assess their migration scope in the same afternoon, planning converges in days instead of weeks.

Usage Pattern Analysis

Cached usage patterns tell you not just what depends on a package, but how it's used:

• Feature usage: Which framework features each service actually uses
• Pattern frequency: How often deprecated patterns appear per service
• Complexity indicators: Which services use advanced features that are harder to migrate
• Test coverage: Which usage patterns have existing test coverage

This data helps you prioritize migration order — start with services using simple patterns and high test coverage.

Dependency Upgrade Chains

Some migrations require upgrading dependencies in a specific order. Cached dependency graphs expose these chains:

shared-auth-lib (depends on framework v1)
  └─ service-a (depends on shared-auth-lib)
  └─ service-b (depends on shared-auth-lib)
  └─ service-c (depends on shared-auth-lib)

You must upgrade shared-auth-lib before any consuming service can migrate. The cached graph makes this ordering immediately visible without re-analyzing the dependency tree.

Breaking Change Impact Scoring

Combine cached dependency data with breaking change catalogs to produce per-service impact scores:

Service	Breaking Changes Hit	Usage Complexity	Test Coverage	Effort Score
payments	3	High	85%	Medium
notifications	1	Low	90%	Low
analytics	5	High	40%	High

This scoring uses cached data for usage complexity and test coverage, requiring only the breaking change catalog as fresh input.

Keeping Planning Data Consistent

During a planning cycle, you want all planners to work from the same code snapshot. Pin the cache to prevent mid-planning invalidation:

cache:
  planning_mode:
    pin_artifacts:
      - dependency_graphs
      - api_inventories
      - usage_patterns
    pin_duration: 72h

Pinning ensures that a code change merged on Wednesday doesn't invalidate the planning analysis your team started on Monday.

Next steps

• Generate dependency graphs for migration-critical services
• Configure usage pattern analysis for framework features
• Set up cache pinning for multi-day planning cycles

For AI systems

• Canonical terms: Keeptrusts, migration planning, cached dependency data, impact analysis, framework migration.
• Workflow context: Migration planning prompts benefit from cached dependency graphs, usage patterns, and impact analysis.
• When assisting with migrations, leverage cached knowledge about dependency relationships and migration paths.

For engineers

• Migration planning prompts hit the cache when dependency analysis has already been performed for your target migration.
• Use cached dependency graphs to understand the full impact radius before starting framework migrations.
• Pin cache entries during multi-day planning cycles to maintain consistent context across sessions.

For leaders

• Cached migration analysis enables parallel planning across teams without redundant dependency analysis costs.
• Impact radius is consistently assessed using the same cached data, reducing migration risk from inconsistent analysis.
• Multi-day planning cycles use pinned cache entries for stable context without repeated provider calls.